{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Day20 进阶构图元素——一页多图（上）\n",
    "\n",
    "　　很多情况下我们制作数据可视化作品时，常常需要在一张画布上绘制出多个独立或者有一定关联的`Axes`对象，即为我们常说的**一页多图**，在接下来几天的日程中我们就将学习到`matplotlib`中构建**一页多图**的常用方式。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-11-07T12:42:24.608401Z",
     "iopub.status.busy": "2020-11-07T12:42:24.608401Z",
     "iopub.status.idle": "2020-11-07T12:42:24.937530Z",
     "shell.execute_reply": "2020-11-07T12:42:24.937530Z",
     "shell.execute_reply.started": "2020-11-07T12:42:24.608401Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "matplotlib版本： 3.1.3\n"
     ]
    }
   ],
   "source": [
    "import matplotlib;print('matplotlib版本：', matplotlib.__version__)\n",
    "import matplotlib.pyplot as plt # 从matplotlib中导入我们最经常使用的pyplot子模\n",
    "\n",
    "plt.rcParams['font.sans-serif'] = ['SimHei'] # 设定默认字体为SimHei以解决中文显示乱码问题\n",
    "plt.rcParams['axes.unicode_minus'] = False # 解决图像负号'-'不正常显示的问题"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "　　在`matplotlib`中创建一页多图最基础的方式其实就是我们这么多天以来，一直用来初始化图像的`subplots()`，通过传入参数`nrows`和`ncols`（默认均为1），可以控制创建m行n列的一页多图："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-11-07T12:42:24.939515Z",
     "iopub.status.busy": "2020-11-07T12:42:24.938518Z",
     "iopub.status.idle": "2020-11-07T12:42:25.296605Z",
     "shell.execute_reply": "2020-11-07T12:42:25.296605Z",
     "shell.execute_reply.started": "2020-11-07T12:42:24.939515Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 创建3行2列\n",
    "fig, ax = plt.subplots(nrows=3, ncols=2, figsize=(6, 6))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "　　我们查看`ax`就可以看到，它的格式是m行n列的`numpy`数组："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-11-07T12:42:25.297558Z",
     "iopub.status.busy": "2020-11-07T12:42:25.297558Z",
     "iopub.status.idle": "2020-11-07T12:42:25.303542Z",
     "shell.execute_reply": "2020-11-07T12:42:25.302545Z",
     "shell.execute_reply.started": "2020-11-07T12:42:25.297558Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x000001B13A5EFA48>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x000001B13BF49C88>],\n",
       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x000001B13C7E5488>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x000001B13C824E48>],\n",
       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x000001B13C858848>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x000001B13C88FA48>]],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ax"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-11-07T12:42:25.304540Z",
     "iopub.status.busy": "2020-11-07T12:42:25.304540Z",
     "iopub.status.idle": "2020-11-07T12:42:25.309526Z",
     "shell.execute_reply": "2020-11-07T12:42:25.308528Z",
     "shell.execute_reply.started": "2020-11-07T12:42:25.304540Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "numpy.ndarray"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(ax)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-11-07T12:42:25.310523Z",
     "iopub.status.busy": "2020-11-07T12:42:25.309526Z",
     "iopub.status.idle": "2020-11-07T12:42:25.315510Z",
     "shell.execute_reply": "2020-11-07T12:42:25.315510Z",
     "shell.execute_reply.started": "2020-11-07T12:42:25.310523Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(3, 2)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ax.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "　　每一个子图上都可以单独绘制图像："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-11-07T12:42:25.317505Z",
     "iopub.status.busy": "2020-11-07T12:42:25.317505Z",
     "iopub.status.idle": "2020-11-07T12:42:25.704470Z",
     "shell.execute_reply": "2020-11-07T12:42:25.704470Z",
     "shell.execute_reply.started": "2020-11-07T12:42:25.317505Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(nrows=3, ncols=2, figsize=(6, 6))\n",
    "\n",
    "ax[0][0].scatter(range(10), range(10))\n",
    "\n",
    "ax[1][0].plot(range(10), range(10))\n",
    "\n",
    "ax[2][1].scatter(range(10), range(10), color='red');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "　　而通过参数`sharex`和`sharey`可以来设置子图之间共享坐标轴的情况："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-11-07T12:42:25.706464Z",
     "iopub.status.busy": "2020-11-07T12:42:25.706464Z",
     "iopub.status.idle": "2020-11-07T12:42:26.087446Z",
     "shell.execute_reply": "2020-11-07T12:42:26.087446Z",
     "shell.execute_reply.started": "2020-11-07T12:42:25.706464Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 所有子图共享x轴，且只有每列最下方子图标记出刻度\n",
    "fig, ax = plt.subplots(nrows=3, ncols=2, figsize=(6, 6), sharex=True)\n",
    "\n",
    "ax[0][0].scatter(range(100), range(100))\n",
    "\n",
    "ax[1][0].plot(range(10), range(10))\n",
    "\n",
    "ax[2][1].scatter(range(10), range(10), color='red');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-11-07T12:42:26.089477Z",
     "iopub.status.busy": "2020-11-07T12:42:26.089477Z",
     "iopub.status.idle": "2020-11-07T12:42:26.458454Z",
     "shell.execute_reply": "2020-11-07T12:42:26.458454Z",
     "shell.execute_reply.started": "2020-11-07T12:42:26.089477Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x432 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 所有子图共享y轴，且只有每行最左边子图标记出刻度\n",
    "fig, ax = plt.subplots(nrows=3, ncols=2, figsize=(6, 6), sharey=True)\n",
    "\n",
    "ax[0][0].scatter(range(100), range(100))\n",
    "\n",
    "ax[1][0].plot(range(10), range(10))\n",
    "\n",
    "ax[2][1].scatter(range(10), range(10), color='red');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-11-07T12:42:26.459451Z",
     "iopub.status.busy": "2020-11-07T12:42:26.459451Z",
     "iopub.status.idle": "2020-11-07T12:42:26.839435Z",
     "shell.execute_reply": "2020-11-07T12:42:26.839435Z",
     "shell.execute_reply.started": "2020-11-07T12:42:26.459451Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 每一列子图共享x轴，y轴同理\n",
    "fig, ax = plt.subplots(nrows=3, ncols=2, figsize=(6, 6), sharex='col')\n",
    "\n",
    "ax[0][0].scatter(range(100), range(100))\n",
    "\n",
    "ax[1][0].plot(range(10), range(10))\n",
    "\n",
    "ax[2][1].scatter(range(10), range(10), color='red');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-11-07T12:42:26.840432Z",
     "iopub.status.busy": "2020-11-07T12:42:26.840432Z",
     "iopub.status.idle": "2020-11-07T12:42:27.232421Z",
     "shell.execute_reply": "2020-11-07T12:42:27.232421Z",
     "shell.execute_reply.started": "2020-11-07T12:42:26.840432Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 每一行子图共享x轴，y轴同理\n",
    "fig, ax = plt.subplots(nrows=3, ncols=2, figsize=(6, 6), sharex='row')\n",
    "\n",
    "ax[0][0].scatter(range(100), range(100))\n",
    "\n",
    "ax[1][0].plot(range(10), range(10))\n",
    "\n",
    "ax[2][1].scatter(range(10), range(10), color='red')\n",
    "ax[2][0].scatter(range(100), range(100), color='green');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 课后测验：\n",
    "\n",
    "　　接下来的时间交给你们，通过本节课的内容，请你结合过往所学的一些知识，在下面代码的基础上，尽量模仿还原出下图：\n",
    "\n",
    "<center>\n",
    "    <img src=\"imgs/课后题目.png\" width=800></img>\n",
    "</center>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-11-07T12:42:27.233382Z",
     "iopub.status.busy": "2020-11-07T12:42:27.233382Z",
     "iopub.status.idle": "2020-11-07T12:42:27.237372Z",
     "shell.execute_reply": "2020-11-07T12:42:27.236374Z",
     "shell.execute_reply.started": "2020-11-07T12:42:27.233382Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x288 with 8 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "from matplotlib import ticker\n",
    "from matplotlib import ticker\n",
    "\n",
    "plt.rcParams[\"font.family\"] = \"Times New Roman\"\n",
    "\n",
    "df = pd.read_csv('kids.csv')\n",
    "state_list = ['Alabama', 'Alaska', 'Arizona', 'Arkansas', \n",
    "              'California', 'Colorado', 'Connecticut', 'Delaware']\n",
    "\n",
    "fig, ax = plt.subplots(nrows=2, ncols=4, \n",
    "                       figsize=(12, 4))\n",
    "\n",
    "# 将二维数组形式的Axes展平\n",
    "ax = [item for couple in ax for item in couple]\n",
    "\n",
    "'''补充你的代码'''\n",
    "\n",
    "df=df[df['variable']=='parkrec']\n",
    "for i,state in enumerate(state_list):\n",
    "    l=df[df['state']==state]\n",
    "    ax[i].plot(l['year'],l['raw'],linestyle='--',linewidth=1,color='black')\n",
    "    ax[i].fill_between(l['year'],l['raw'],0,color='teal',alpha=0.9)\n",
    "    ax[i].set_title(state)\n",
    "    ax[i].set_xlim(1997,2016)\n",
    "    ax[i].set_xticks([1997,2001,2006,2011,2016])\n",
    "    ax[i].tick_params(axis='x', which='major',labelrotation=45)\n",
    "    ax[i].yaxis.set_major_locator(ticker.NullLocator())\n",
    "    ax[i].spines['left'].set_color('none')\n",
    "    ax[i].spines['right'].set_color('none')\n",
    "    ax[i].spines['top'].set_color('none')\n",
    "\n",
    "    \n",
    "fig.set_facecolor('white')\n",
    "\n",
    "# 调整子图之间的距离，明天的日程会详细介绍\n",
    "fig.subplots_adjust(hspace=0.6, wspace=0.1) \n",
    "fig.savefig('课后题目.png', dpi=300, pad_inches=0, bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* 请在今天对应的帖子下回复你的代码和结果"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>state</th>\n",
       "      <th>variable</th>\n",
       "      <th>year</th>\n",
       "      <th>raw</th>\n",
       "      <th>inf_adj</th>\n",
       "      <th>inf_adj_perchild</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Alabama</td>\n",
       "      <td>PK12ed</td>\n",
       "      <td>1997</td>\n",
       "      <td>3271969.0</td>\n",
       "      <td>4.665308e+06</td>\n",
       "      <td>3.929449</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Alaska</td>\n",
       "      <td>PK12ed</td>\n",
       "      <td>1997</td>\n",
       "      <td>1042311.0</td>\n",
       "      <td>1.486170e+06</td>\n",
       "      <td>7.548493</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Arizona</td>\n",
       "      <td>PK12ed</td>\n",
       "      <td>1997</td>\n",
       "      <td>3388165.0</td>\n",
       "      <td>4.830986e+06</td>\n",
       "      <td>3.706679</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Arkansas</td>\n",
       "      <td>PK12ed</td>\n",
       "      <td>1997</td>\n",
       "      <td>1960613.0</td>\n",
       "      <td>2.795523e+06</td>\n",
       "      <td>3.891275</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>California</td>\n",
       "      <td>PK12ed</td>\n",
       "      <td>1997</td>\n",
       "      <td>28708364.0</td>\n",
       "      <td>4.093357e+07</td>\n",
       "      <td>4.282325</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Colorado</td>\n",
       "      <td>PK12ed</td>\n",
       "      <td>1997</td>\n",
       "      <td>3332994.0</td>\n",
       "      <td>4.752320e+06</td>\n",
       "      <td>4.380313</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Connecticut</td>\n",
       "      <td>PK12ed</td>\n",
       "      <td>1997</td>\n",
       "      <td>4014870.0</td>\n",
       "      <td>5.724568e+06</td>\n",
       "      <td>6.697437</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Delaware</td>\n",
       "      <td>PK12ed</td>\n",
       "      <td>1997</td>\n",
       "      <td>776825.0</td>\n",
       "      <td>1.107629e+06</td>\n",
       "      <td>5.625310</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>District of Columbia</td>\n",
       "      <td>PK12ed</td>\n",
       "      <td>1997</td>\n",
       "      <td>544051.0</td>\n",
       "      <td>7.757304e+05</td>\n",
       "      <td>6.105133</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Florida</td>\n",
       "      <td>PK12ed</td>\n",
       "      <td>1997</td>\n",
       "      <td>11498394.0</td>\n",
       "      <td>1.639488e+07</td>\n",
       "      <td>4.453578</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  state variable  year         raw       inf_adj  \\\n",
       "0               Alabama   PK12ed  1997   3271969.0  4.665308e+06   \n",
       "1                Alaska   PK12ed  1997   1042311.0  1.486170e+06   \n",
       "2               Arizona   PK12ed  1997   3388165.0  4.830986e+06   \n",
       "3              Arkansas   PK12ed  1997   1960613.0  2.795523e+06   \n",
       "4            California   PK12ed  1997  28708364.0  4.093357e+07   \n",
       "5              Colorado   PK12ed  1997   3332994.0  4.752320e+06   \n",
       "6           Connecticut   PK12ed  1997   4014870.0  5.724568e+06   \n",
       "7              Delaware   PK12ed  1997    776825.0  1.107629e+06   \n",
       "8  District of Columbia   PK12ed  1997    544051.0  7.757304e+05   \n",
       "9               Florida   PK12ed  1997  11498394.0  1.639488e+07   \n",
       "\n",
       "   inf_adj_perchild  \n",
       "0          3.929449  \n",
       "1          7.548493  \n",
       "2          3.706679  \n",
       "3          3.891275  \n",
       "4          4.282325  \n",
       "5          4.380313  \n",
       "6          6.697437  \n",
       "7          5.625310  \n",
       "8          6.105133  \n",
       "9          4.453578  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "l=df[df['state']==state_list[0]].groupby('year').agg('sum')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Int64Index([1997, 1998, 1999, 2000, 2001, 2002, 2003, 2004, 2005, 2006, 2007,\n",
       "            2008, 2009, 2010, 2011, 2012, 2013, 2014, 2015, 2016],\n",
       "           dtype='int64', name='year')"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "l.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1b13df1db88>]"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ax.plot(l.index,l['raw'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>raw</th>\n",
       "      <th>inf_adj</th>\n",
       "      <th>inf_adj_perchild</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>year</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1997</th>\n",
       "      <td>1.818314e+06</td>\n",
       "      <td>2.592627e+06</td>\n",
       "      <td>13.167158</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1998</th>\n",
       "      <td>2.215203e+06</td>\n",
       "      <td>3.120058e+06</td>\n",
       "      <td>15.574592</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1999</th>\n",
       "      <td>2.303881e+06</td>\n",
       "      <td>3.204305e+06</td>\n",
       "      <td>15.736693</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000</th>\n",
       "      <td>2.454057e+06</td>\n",
       "      <td>3.344048e+06</td>\n",
       "      <td>16.204769</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2001</th>\n",
       "      <td>2.723141e+06</td>\n",
       "      <td>3.623977e+06</td>\n",
       "      <td>17.418697</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002</th>\n",
       "      <td>2.990270e+06</td>\n",
       "      <td>3.916100e+06</td>\n",
       "      <td>18.733016</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2003</th>\n",
       "      <td>3.252676e+06</td>\n",
       "      <td>4.179871e+06</td>\n",
       "      <td>19.855264</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2004</th>\n",
       "      <td>3.543182e+06</td>\n",
       "      <td>4.443125e+06</td>\n",
       "      <td>20.942728</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2005</th>\n",
       "      <td>3.776216e+06</td>\n",
       "      <td>4.591177e+06</td>\n",
       "      <td>21.404688</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2006</th>\n",
       "      <td>4.070162e+06</td>\n",
       "      <td>4.792672e+06</td>\n",
       "      <td>22.128265</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007</th>\n",
       "      <td>4.183867e+06</td>\n",
       "      <td>4.796326e+06</td>\n",
       "      <td>21.950044</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008</th>\n",
       "      <td>4.471011e+06</td>\n",
       "      <td>5.021283e+06</td>\n",
       "      <td>22.822560</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009</th>\n",
       "      <td>4.789363e+06</td>\n",
       "      <td>5.316960e+06</td>\n",
       "      <td>24.157677</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010</th>\n",
       "      <td>5.189328e+06</td>\n",
       "      <td>5.710730e+06</td>\n",
       "      <td>26.052838</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011</th>\n",
       "      <td>5.340283e+06</td>\n",
       "      <td>5.759806e+06</td>\n",
       "      <td>26.371892</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012</th>\n",
       "      <td>5.790051e+06</td>\n",
       "      <td>6.132891e+06</td>\n",
       "      <td>28.142982</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013</th>\n",
       "      <td>5.800443e+06</td>\n",
       "      <td>6.041866e+06</td>\n",
       "      <td>27.911383</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014</th>\n",
       "      <td>6.036930e+06</td>\n",
       "      <td>6.175213e+06</td>\n",
       "      <td>28.576510</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015</th>\n",
       "      <td>6.275090e+06</td>\n",
       "      <td>6.344007e+06</td>\n",
       "      <td>29.258786</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016</th>\n",
       "      <td>6.456673e+06</td>\n",
       "      <td>6.456673e+06</td>\n",
       "      <td>29.733289</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               raw       inf_adj  inf_adj_perchild\n",
       "year                                              \n",
       "1997  1.818314e+06  2.592627e+06         13.167158\n",
       "1998  2.215203e+06  3.120058e+06         15.574592\n",
       "1999  2.303881e+06  3.204305e+06         15.736693\n",
       "2000  2.454057e+06  3.344048e+06         16.204769\n",
       "2001  2.723141e+06  3.623977e+06         17.418697\n",
       "2002  2.990270e+06  3.916100e+06         18.733016\n",
       "2003  3.252676e+06  4.179871e+06         19.855264\n",
       "2004  3.543182e+06  4.443125e+06         20.942728\n",
       "2005  3.776216e+06  4.591177e+06         21.404688\n",
       "2006  4.070162e+06  4.792672e+06         22.128265\n",
       "2007  4.183867e+06  4.796326e+06         21.950044\n",
       "2008  4.471011e+06  5.021283e+06         22.822560\n",
       "2009  4.789363e+06  5.316960e+06         24.157677\n",
       "2010  5.189328e+06  5.710730e+06         26.052838\n",
       "2011  5.340283e+06  5.759806e+06         26.371892\n",
       "2012  5.790051e+06  6.132891e+06         28.142982\n",
       "2013  5.800443e+06  6.041866e+06         27.911383\n",
       "2014  6.036930e+06  6.175213e+06         28.576510\n",
       "2015  6.275090e+06  6.344007e+06         29.258786\n",
       "2016  6.456673e+06  6.456673e+06         29.733289"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "l"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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